Intrafractional motion models based on principal components in Magnetic Resonance guided prostate radiotherapy.

Intrafractional motion Principal component analysis Prostate cancer

Journal

Physics and imaging in radiation oncology
ISSN: 2405-6316
Titre abrégé: Phys Imaging Radiat Oncol
Pays: Netherlands
ID NLM: 101704276

Informations de publication

Date de publication:
Oct 2021
Historique:
received: 20 01 2021
revised: 15 09 2021
accepted: 15 09 2021
entrez: 18 10 2021
pubmed: 19 10 2021
medline: 19 10 2021
Statut: epublish

Résumé

Devices that combine an MR-scanner with a Linac for radiotherapy, referred to as MR-Linac systems, introduce the possibility to acquire high resolution images prior and during treatment. Hence, there is a possibility to acquire individualised learning sets for motion models for each fraction and the construction of intrafractional motion models. We investigated the feasibility for a principal component analysis (PCA) based, intrafractional motion model of the male pelvic region. 4D-scans of nine healthy male volunteers were utilized, FOV covering the entire pelvic region including prostate, bladder and rectum with manual segmentation of each organ at each time frame. Deformable image registration with an optical flow algorithm was performed for each subject with the first time frame as reference. PCA was performed on a subset of the resulting displacement vector fields to construct individualised motion models evaluated on the remaining fields. The registration algorithm produced accurate registration result, in general DICE overlap An individualised intrafractional male pelvic motion model is feasible. Geometric accuracy was about 1 mm based on 1-2 principal components.

Sections du résumé

BACKGROUND AND PURPOSE OBJECTIVE
Devices that combine an MR-scanner with a Linac for radiotherapy, referred to as MR-Linac systems, introduce the possibility to acquire high resolution images prior and during treatment. Hence, there is a possibility to acquire individualised learning sets for motion models for each fraction and the construction of intrafractional motion models. We investigated the feasibility for a principal component analysis (PCA) based, intrafractional motion model of the male pelvic region.
MATERIALS AND METHODS METHODS
4D-scans of nine healthy male volunteers were utilized, FOV covering the entire pelvic region including prostate, bladder and rectum with manual segmentation of each organ at each time frame. Deformable image registration with an optical flow algorithm was performed for each subject with the first time frame as reference. PCA was performed on a subset of the resulting displacement vector fields to construct individualised motion models evaluated on the remaining fields.
RESULTS RESULTS
The registration algorithm produced accurate registration result, in general DICE overlap
CONCLUSIONS CONCLUSIONS
An individualised intrafractional male pelvic motion model is feasible. Geometric accuracy was about 1 mm based on 1-2 principal components.

Identifiants

pubmed: 34660917
doi: 10.1016/j.phro.2021.09.004
pii: S2405-6316(21)00054-3
pmc: PMC8502906
doi:

Types de publication

Journal Article

Langues

eng

Pagination

17-22

Informations de copyright

© 2021 The Authors.

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Auteurs

Samuel Fransson (S)

Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Department of Medical Physics, Akademiska Hospital, Uppsala, Sweden.

David Tilly (D)

Department of Medical Physics, Akademiska Hospital, Uppsala, Sweden.
Elekta Instruments AB, Stockholm, Sweden.
Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.

Anders Ahnesjö (A)

Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.

Tufve Nyholm (T)

Department of Radiation Sciences, Umeå University, Umeå, Sweden.

Robin Strand (R)

Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Department of Information Technology, Uppsala University, Uppsala, Sweden.

Classifications MeSH